LON:DCC DCC PLC

Outlook: DCC PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 10 Mar 2023 for (n+1 year)
Methodology : Multi-Task Learning (ML)

Abstract

DCC PLC prediction model is evaluated with Multi-Task Learning (ML) and Multiple Regression^1,2,3,4 and it is concluded that the LON:DCC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Key Points

1. Prediction Modeling
2. Trust metric by Neural Network
3. Is it better to buy and sell or hold?

LON:DCC Target Price Prediction Modeling Methodology

We consider DCC PLC Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of LON:DCC stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^1,2,3,4

F(Multiple Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Task Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:DCC stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do AC Investment Research machine learning (predictive) algorithms actually work?

LON:DCC Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:DCC DCC PLC
Time series to forecast n: 10 Mar 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

IFRS Reconciliation Adjustments for DCC PLC

1. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
2. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
3. The methods used to determine whether credit risk has increased significantly on a financial instrument since initial recognition should consider the characteristics of the financial instrument (or group of financial instruments) and the default patterns in the past for comparable financial instruments. Despite the requirement in paragraph 5.5.9, for financial instruments for which default patterns are not concentrated at a specific point during the expected life of the financial instrument, changes in the risk of a default occurring over the next 12 months may be a reasonable approximation of the changes in the lifetime risk of a default occurring. In such cases, an entity may use changes in the risk of a default occurring over the next 12 months to determine whether credit risk has increased significantly since initial recognition, unless circumstances indicate that a lifetime assessment is necessary
4. The decision of an entity to designate a financial asset or financial liability as at fair value through profit or loss is similar to an accounting policy choice (although, unlike an accounting policy choice, it is not required to be applied consistently to all similar transactions). When an entity has such a choice, paragraph 14(b) of IAS 8 requires the chosen policy to result in the financial statements providing reliable and more relevant information about the effects of transactions, other events and conditions on the entity's financial position, financial performance or cash flows. For example, in the case of designation of a financial liability as at fair value through profit or loss, paragraph 4.2.2 sets out the two circumstances when the requirement for more relevant information will be met. Accordingly, to choose such designation in accordance with paragraph 4.2.2, the entity needs to demonstrate that it falls within one (or both) of these two circumstances.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

DCC PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. DCC PLC prediction model is evaluated with Multi-Task Learning (ML) and Multiple Regression^1,2,3,4 and it is concluded that the LON:DCC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

LON:DCC DCC PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Baa2
Balance Sheet	B2	Caa2
Leverage Ratios	C	B2
Cash Flow	Caa2	Ba1
Rates of Return and Profitability	Baa2	Caa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 521 signals.

References

1. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
2. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
3. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
4. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
5. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
6. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
7. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]

Frequently Asked Questions

Q: What is the prediction methodology for LON:DCC stock?
A: LON:DCC stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Multiple Regression
Q: Is LON:DCC stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:DCC Stock.
Q: Is DCC PLC stock a good investment?
A: The consensus rating for DCC PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:DCC stock?
A: The consensus rating for LON:DCC is Sell.
Q: What is the prediction period for LON:DCC stock?
A: The prediction period for LON:DCC is (n+1 year)

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